###Agenda

###Getting started: birthwt data set - We’re going to start by operating on the birthwt dataset from the MASS library

library(MASS)
str(birthwt)
## 'data.frame':    189 obs. of  10 variables:
##  $ low  : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ age  : int  19 33 20 21 18 21 22 17 29 26 ...
##  $ lwt  : int  182 155 105 108 107 124 118 103 123 113 ...
##  $ race : int  2 3 1 1 1 3 1 3 1 1 ...
##  $ smoke: int  0 0 1 1 1 0 0 0 1 1 ...
##  $ ptl  : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ ht   : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ ui   : int  1 0 0 1 1 0 0 0 0 0 ...
##  $ ftv  : int  0 3 1 2 0 0 1 1 1 0 ...
##  $ bwt  : int  2523 2551 2557 2594 2600 2622 2637 2637 2663 2665 ...

###Renaming the variables - The dataset doesn’t come with very descriptive variable names

colnames(birthwt) 
##  [1] "low"   "age"   "lwt"   "race"  "smoke" "ptl"   "ht"    "ui"    "ftv"
## [10] "bwt"
# The default names are not very descriptive

colnames(birthwt) <- c("birthwt.below.2500", "mother.age", "mother.weight",
    "race", "mother.smokes", "previous.prem.labor", "hypertension", "uterine.irr",
    "physician.visits", "birthwt.grams")

# Better names!

###Renaming the factors - All the factors are currently represented as integers

library(plyr)
birthwt <- transform(birthwt,
            race = as.factor(mapvalues(race, c(1, 2, 3),
                              c("white","black", "other"))),
            mother.smokes = as.factor(mapvalues(mother.smokes,
                              c(0,1), c("no", "yes"))),
            hypertension = as.factor(mapvalues(hypertension,
                              c(0,1), c("no", "yes"))),
            uterine.irr = as.factor(mapvalues(uterine.irr,
                              c(0,1), c("no", "yes"))),
            birthwt.below.2500 = as.factor(mapvalues(birthwt.below.2500,
                              c(0,1), c("no", "yes")))
            )

###Summary of the data - Now that things are coded correctly, we can look at an overall summary

summary(birthwt)
##  birthwt.below.2500   mother.age    mother.weight      race    mother.smokes
##  no :130            Min.   :14.00   Min.   : 80.0   black:26   no :115
##  yes: 59            1st Qu.:19.00   1st Qu.:110.0   other:67   yes: 74
##                     Median :23.00   Median :121.0   white:96
##                     Mean   :23.24   Mean   :129.8
##                     3rd Qu.:26.00   3rd Qu.:140.0
##                     Max.   :45.00   Max.   :250.0
##  previous.prem.labor hypertension uterine.irr physician.visits birthwt.grams
##  Min.   :0.0000      no :177      no :161     Min.   :0.0000   Min.   : 709
##  1st Qu.:0.0000      yes: 12      yes: 28     1st Qu.:0.0000   1st Qu.:2414
##  Median :0.0000                               Median :0.0000   Median :2977
##  Mean   :0.1958                               Mean   :0.7937   Mean   :2945
##  3rd Qu.:0.0000                               3rd Qu.:1.0000   3rd Qu.:3487
##  Max.   :3.0000                               Max.   :6.0000   Max.   :4990

###A simple table - Let’s use the tapply() function to see what the average birthweight looks like when broken down by race and smoking status

with(birthwt, tapply(birthwt.grams, INDEX = list(race, mother.smokes), FUN = mean)) 
##             no      yes
## black 2854.500 2504.000
## other 2815.782 2757.167
## white 3428.750 2826.846

###What if we wanted nicer looking output? - Let’s use the header {r, results='asis'}, along with the kable() function from the knitr library

library(knitr)
bwt.tbl <- with(birthwt, tapply(birthwt.grams, INDEX = list(race, mother.smokes), FUN = mean))
kable(bwt.tbl, format = "markdown")
no yes
black 2854.500 2504.000
other 2815.782 2757.167
white 3428.750 2826.846

###aggregate() function - Let’s first recall what tapply() does

###Example: tapply vs aggregate

library(MASS)
with(birthwt, tapply(birthwt.grams, INDEX = list(race, mother.smokes), FUN = mean)) # tapply
##             no      yes
## black 2854.500 2504.000
## other 2815.782 2757.167
## white 3428.750 2826.846
with(birthwt, aggregate(birthwt.grams, by = list(race, mother.smokes), FUN = mean)) # aggregate
##   Group.1 Group.2        x
## 1   black      no 2854.500
## 2   other      no 2815.782
## 3   white      no 3428.750
## 4   black     yes 2504.000
## 5   other     yes 2757.167
## 6   white     yes 2826.846

###Example: different syntax - Here’s a convenient alternative way to call aggregate

aggregate(birthwt.grams ~ race + mother.smokes, FUN=mean, data=birthwt)
##    race mother.smokes birthwt.grams
## 1 black            no      2854.500
## 2 other            no      2815.782
## 3 white            no      3428.750
## 4 black           yes      2504.000
## 5 other           yes      2757.167
## 6 white           yes      2826.846

###A closer look at low birth weight

weight.smoke.tbl <- with(birthwt, table(birthwt.below.2500, mother.smokes))
weight.smoke.tbl
##                   mother.smokes
## birthwt.below.2500 no yes
##                no  86  44
##                yes 29  30
or.smoke.bwt <- (weight.smoke.tbl[2,2] / weight.smoke.tbl[1,2]) / (weight.smoke.tbl[2,1] / weight.smoke.tbl[1,1])
or.smoke.bwt
## [1] 2.021944

###continuted…

with(birthwt, cor(birthwt.grams, mother.age))  # Calculate correlation
## [1] 0.09031781
with(birthwt, cor(birthwt.grams[mother.smokes == "yes"], mother.age[mother.smokes == "yes"]))
## [1] -0.1441649
with(birthwt, cor(birthwt.grams[mother.smokes == "no"], mother.age[mother.smokes == "no"]))
## [1] 0.2014558

###Faster way: by() function - Think of the by(data, INDICES, FUN) function as a tapply() function that operates on data frames instead of just vectors

by(data = birthwt[c("birthwt.grams", "mother.age")],
   INDICES = birthwt["mother.smokes"],
   FUN = function(x) {cor(x[,1], x[,2])})
## mother.smokes: no
## [1] 0.2014558
## ------------------------------------------------------------
## mother.smokes: yes
## [1] -0.1441649

###Standard graphics in R

Single-variable plots

Let’s continue with the birthwt data from the MASS library.

Here are some basic single-variable plots.

par(mfrow = c(2,2)) # Display plots in a single 2 x 2 figure 
plot(birthwt$mother.age)
with(birthwt, hist(mother.age))
plot(birthwt$mother.smokes)
plot(birthwt$birthwt.grams)

Note that the result of calling plot(x, ...) varies depending on what x is.
- When x is numeric, you get a plot showing the value of x at every index.
- When x is a factor, you get a bar plot of counts for every level

Let’s add more information to the smoking bar plot, and also change the color by setting the col option.

par(mfrow = c(1,1))
plot(birthwt$mother.smokes,
     main = "Mothers Who Smoked In Pregnancy",
     xlab = "Smoking during pregnancy",
     ylab = "Count of Mothers",
     col = "lightgrey")

(much) better graphics with ggplot2

Introduction to ggplot2

ggplot2 has a slightly steeper learning curve than the base graphics functions, but it also generally produces far better and more easily customizable graphics.

There are two basic calls in ggplot:

  • qplot(x, y, ..., data): a “quick-plot” routine, which essentially replaces the base plot()
  • ggplot(data, aes(x, y, ...), ...): defines a graphics object from which plots can be generated, along with aesthetic mappings that specify how variables are mapped to visual properties.
library(ggplot2)
## Registered S3 methods overwritten by 'tibble':
##   method     from
##   format.tbl pillar
##   print.tbl  pillar

plot vs qplot

Here’s how the default scatterplots look in ggplot compared to the base graphics. We’ll illustrate things by continuing to use the birthwt data from the MASS library.

with(birthwt, plot(mother.age, birthwt.grams))  # Base graphics 

qplot(x=mother.age, y=birthwt.grams, data=birthwt)  # using qplot from ggplot2

Remember how it took us some effort last time to add color coding, use different plotting characters, and add a legend? Here’s the qplot call that does it all in one simple line.

qplot(x=mother.age, y=birthwt.grams, data=birthwt,
      color = mother.smokes,
      shape = mother.smokes,
      xlab = "Mother's age (years)",
      ylab = "Baby's birthweight (grams)"
      ) 

This way you won’t run into problems of accidentally producing the wrong legend. The legend is produced based on the colour and shape argument that you pass in. (Note: color and colour have the same effect. )

ggplot function

The ggplot2 library comes with a dataset called diamonds. Let’s look at it

dim(diamonds)
## [1] 53940    10
head(diamonds)
## Warning: `...` is not empty.
##
## We detected these problematic arguments:
## * `needs_dots`
##
## These dots only exist to allow future extensions and should be empty.
## Did you misspecify an argument?
## # A tibble: 6 x 10
##   carat cut       color clarity depth table price     x     y     z
##   <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1  0.23 Ideal     E     SI2      61.5    55   326  3.95  3.98  2.43
## 2  0.21 Premium   E     SI1      59.8    61   326  3.89  3.84  2.31
## 3  0.23 Good      E     VS1      56.9    65   327  4.05  4.07  2.31
## 4  0.29 Premium   I     VS2      62.4    58   334  4.2   4.23  2.63
## 5  0.31 Good      J     SI2      63.3    58   335  4.34  4.35  2.75
## 6  0.24 Very Good J     VVS2     62.8    57   336  3.94  3.96  2.48

It is a data frame of 53,940 diamonds, recording their attributes such as carat, cut, color, clarity, and price.

We will make a scatterplot showing the price as a function of the carat (size). (The data set is large so the plot may take a few moments to generate.)

diamond.plot <- ggplot(data=diamonds, aes(x=carat, y=price))
diamond.plot + geom_point()

The data set looks a little weird because a lot of diamonds are concentrated on the 1, 1.5 and 2 carat mark.

Let’s take a step back and try to understand the ggplot syntax.

  1. The first thing we did was to define a graphics object, diamond.plot. This definition told R that we’re using the diamonds data, and that we want to display carat on the x-axis, and price on the y-axis.

  2. We then called diamond.plot + geom_point() to get a scatterplot.

The arguments passed to aes() are called mappings. Mappings specify what variables are used for what purpose. When you use geom_point() in the second line, it pulls x, y, colour, size, etc., from the mappings specified in the ggplot() command.

You can also specify some arguments to geom_point directly if you want to specify them for each plot separately instead of pre-specifying a default.

Here we shrink the points to a smaller size, and use the alpha argument to make the points transparent.

diamond.plot + geom_point(size = 0.7, alpha = 0.3)

If we wanted to let point color depend on the color indicator of the diamond, we could do so in the following way.

diamond.plot <- ggplot(data=diamonds, aes(x=carat, y=price, colour = color))
diamond.plot + geom_point()

If we didn’t know anything about diamonds going in, this plot would indicate to us that D is likely the highest diamond grade, while J is the lowest grade.

We can change colors by specifying a different color palette. Here’s how we can switch to the cbPalette we saw last class.

cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
diamond.plot <- ggplot(data=diamonds, aes(x=carat, y=price, colour = color))
diamond.plot + geom_point() + scale_colour_manual(values=cbPalette)

To make the scatterplot look more typical, we can switch to logarithmic coordinate axis spacing.

diamond.plot + geom_point() +
  coord_trans(x = "log10", y = "log10")

Conditional plots

We can create plots showing the relationship between variables across different values of a factor. For instance, here’s a scatterplot showing how diamond price varies with carat size, conditioned on color. It’s created using the facet_wrap(~ factor1 + factor2 + ... + factorn) command.

diamond.plot <- ggplot(data=diamonds, aes(x=carat, y=price, colour = color))

diamond.plot + geom_point() + facet_wrap(~ cut)

You can also use facet_grid() to produce this type of output.

diamond.plot + geom_point() + facet_grid(. ~ cut)

diamond.plot + geom_point() + facet_grid(cut ~ .)

ggplot can create a lot of different kinds of plots, just like lattice. Here are some examples.

Function Description
geom_point(...) Points, i.e., scatterplot
geom_bar(...) Bar chart
geom_line(...) Line chart
geom_boxplot(...) Boxplot
geom_violin(...) Violin plot
geom_density(...) Density plot with one variable
geom_density2d(...) Density plot with two variables
geom_histogram(...) Histogram

A bar chart

qplot(x = race, data = birthwt, geom = "bar")

Histograms and density plots

base.plot <- ggplot(birthwt, aes(x = mother.age)) +
  xlab("Mother's age")
base.plot + geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

base.plot + geom_histogram(aes(fill = race))
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

base.plot + geom_density()

base.plot + geom_density(aes(fill = race), alpha = 0.5)

Box plots and violin plots

base.plot <- ggplot(birthwt, aes(x = as.factor(physician.visits), y = birthwt.grams)) +
  xlab("Number of first trimester physician visits") +
  ylab("Baby's birthweight (grams)")

# Box plot
base.plot + geom_boxplot()

# Violin plot
base.plot + geom_violin()

Visualizing means

Previously we calculated the following table:

bwt.summary <- aggregate(birthwt.grams ~ race + mother.smokes, data = birthwt, FUN = mean) # aggregate
bwt.summary
##    race mother.smokes birthwt.grams
## 1 black            no      2854.500
## 2 other            no      2815.782
## 3 white            no      3428.750
## 4 black           yes      2504.000
## 5 other           yes      2757.167
## 6 white           yes      2826.846

We can plot this table in a nice bar chart as follows:

# Define basic aesthetic parameters
p.bwt <- ggplot(data = bwt.summary, aes(y = birthwt.grams, x = race, fill = mother.smokes))

# Pick colors for the bars
bwt.colors <- c("#009E73", "#999999")

# Display barchart
p.bwt + geom_bar(stat = "identity", position = "dodge") +
  ylab("Average birthweight") +
  xlab("Mother's race") +
  guides(fill = guide_legend(title = "Mother's smoking status")) +
  scale_fill_manual(values=bwt.colors)

Does the association between birthweight and mother’s age depend on smoking status?

We previously ran the following command to calculate the correlation between mother’s ages and baby birthweights.

by(data = birthwt[c("birthwt.grams", "mother.age")],
   INDICES = birthwt["mother.smokes"],
   FUN = function(x) {cor(x[,1], x[,2])})
## mother.smokes: no
## [1] 0.2014558
## ------------------------------------------------------------
## mother.smokes: yes
## [1] -0.1441649

Here’s a visualization of our data that allows us to see what’s going on.

ggplot(birthwt, aes(x=mother.age, y=birthwt.grams, shape=mother.smokes, color=mother.smokes)) +
  geom_point() + # Adds points (scatterplot)
  geom_smooth(method = "lm") + # Adds regression lines
  ylab("Birth Weight (grams)") + # Changes y-axis label
  xlab("Mother's Age (years)") + # Changes x-axis label
  ggtitle("Birth Weight by Mother's Age") # Changes plot title
## `geom_smooth()` using formula 'y ~ x'